Obesity, a growing epidemic in the US and a health care priority in Healthy People 2015, is a risk factor for type 2 diabetes and cardiovascular disease. In recent work we have shown that in our cohort of 100 kidney transplant recipients, over half (56%) gained weight with the average amount of 9 kg., which is significantly more than the 1 kg average weight gain in US adults. This predictable and significant weight gain within a short amount of time, and its association with morbidity and mortality, makes this a high priority concern. The focus of this research program is on discovery and utilization of biomarkers in combination with behavioral, environmental and clinical factors to predict clinical outcomes, with the long term goal to guide therapies in various patient populations. For example, obesity, a growing epidemic in the US and a health care priority in Healthy People 2015, is a risk factor for type 2 diabetes and cardiovascular disease. In recent work we have shown that in our cohort of 100 kidney transplant recipients, over half (56%) gained weight with the average amount of 9 kg., which is significantly more than the 1 kg average weight gain in US adults. This predictable and significant weight gain within a short amount of time, and its association with morbidity and mortality, makes this a high priority concern. Using the NIH-Symptom Science Model to guide us, we have conducted studies on samples prospectively obtained from kidney transplant recipients. We examined genomic, proteomic, and environmental factors (food intake, physical activity, demographic, health status, psychosocial) contributing to obesity at one year following renal transplantation in recipients. Long-term goals include prevention and treatment of obesity in recipients. Our hypothesis is that gene- environmental interactions can predict whether individuals will gain weight/become obese at one year post-transplant. Specifically we will (1) identify environmental factors associated with post-transplant weight gain, (2) identify gene expressions associated with weight gain, (3) use Bayesian analysis to determine combinations of gene- environment interactions that predict weight gain and obesity. A prospective design was used to compare genetic and environmental factors and clinical outcomes at baseline, 3, 6, and 12 months post-transplant. Gene expression profiling using microarray analysis and real-time polymerase chain reaction on adipose tissue was used to identify key regulatory elements that play a major role in obesity. Bayesian Network modeling was used to investigate causal relationships. This significant and innovative study incorporates an interdisciplinary approach to combine emerging genomic and bioinformatic technologies with traditional methodologies to explicate key gene-environment interactions responsible for post-transplant obesity. The relevance of this study is that findings will assist health care practitioners in caring for renal transplant recipients so that they do not gain weight and become obese following renal transplantation. This will result in fewer health care problems following transplantation. Our recent studies and publications have reported on findings including 1) characterization of body composition and fat mass distribution in 1 year after kidney transplantation, 2) a prospective study of depression and weight change after kidney transplantation. Studies and publications related to biomarker discovery in other populations include 1) sports-related concussion results in differential expression of inflammatory genes in peripheral blood during the acute and sub-acute period, 2) development of an analysis pipeline characterizing the oral microbiome in severe aplastic anemia patients, and 3) the association between DNA-methylation profile and PTSD symptom in military personnel. Future work will explore the ability of a signature (ie, biomarker) to further explore the underlying biologic mechanisms and to provide therapeutic guidance to monitor treatment and prevent/modify adverse outcomes.